4.7 Article

Advanced Algorithm for Reliable Quantification of the Geometry and Printability of Printed Patterns

Journal

NANOMATERIALS
Volume 13, Issue 10, Pages -

Publisher

MDPI
DOI: 10.3390/nano13101597

Keywords

printed electronics; printing; geometry; printability; quantification

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This study introduces an advanced algorithm for accurate measurement of the geometry and printability of shape patterns to establish an international standard. The algorithm involves three core concepts: extraction of edges of printed patterns and identification of pixel positions, identification of reference edges via the best-fitting of the shape pattern, and calculation of different pixel positions of edges related to reference edges.
In nanoparticle-based printed electronic devices, the printability of the patterns constituting the device are crucial factors. Although many studies have investigated the printability of patterns, only a few have analyzed and established international standards for measuring the dimensions and printability of shape patterns. This study introduces an advanced algorithm for accurate measurement of the geometry and printability of shape patterns to establish an international standard for pattern dimensions and printability. The algorithm involves three core concepts: extraction of edges of printed patterns and identification of pixel positions, identification of reference edges via the best-fitting of the shape pattern, and calculation of different pixel positions of edges related to reference edges. This method enables the measurement of the pattern geometry and printability, including edge waviness and widening, while considering all pixels comprising the edges of the patterns. The study results revealed that the rectangle and circle patterns exhibited an average widening of 3.55% and a maximum deviation of 1.58%, based on an average of 1662 data points. This indicates that the algorithm has potential applications in real-time pattern quality evaluation, process optimization using statistical or AI-based methods, and foundation of International Electrotechnical Commission standards for shape patterns.

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